Neural net modeling has gradually become a preferred approach in building a mathematical model of a system, especially when the dynamics of the system are unfamiliar to the model builder. This is due at least in part to its capability of achieving universal approximation. Being a mathematical model of a system, a neural net should be representative of the dynamics of the system. Because a neural net model is created from a set of training data representing the system dynamics, the power of representation the model has for representing the system cannot be better than that embedded in that set of training data. However, the reliability, or other characteristics affecting the quality of the resulting model, of each pattern in a data set may not be the same due to various reasons, such as equipment constraints or uneven distribution of data points.